ping

Add debug print statement in reassess_dupes function to log detailed calculations for ownership adjustments, aiding in troubleshooting and validation of metrics.

Update data type handling for salary, median, and ownership difference calculations in reassess_edge.py to improve memory efficiency and precision.

Refactor salary, median, and ownership calculations in app.py to handle GOLF sport variable separately, improving clarity and accuracy in data processing.

Update reassess_dupes function in reassess_edge.py to adjust ownership calculation by modifying the salary adjustment factor, enhancing accuracy in ownership metrics.

Remove debug print statement from calculate_weighted_ownership_single_row and fix geomean calculation in reassess_edge function for improved accuracy in ownership metrics.

Add debug print statement for ownership values in calculate_weighted_ownership_single_row to aid in troubleshooting and validation of ownership calculations.

Refactor ownership value calculation in calculate_weighted_ownership_single_row to use pd.to_numeric for better handling of non-numeric values, improving robustness in edge reassessment logic.

Refactor calculate_weighted_ownership function to operate on a single row of ownership values, improving clarity and performance in the reassess_edge function.

Optimize ownership calculation in reassess_edge function by directly applying calculate_weighted_ownership_wrapper to selected lineups, improving performance and readability.

Fix off-by-one error in own_columns generation in reassess_edge function to ensure correct player ownership mapping.

Update reassess_edge function to include maps_dict parameter for improved player ownership mapping in app.py, enhancing edge reassessment logic.

Update num_players calculation in reassess_edge function to use salary column index, improving accuracy in player data processing.

Refactor reassess_lineup_edge function in global_func/reassess_edge.py to simplify the calculation of 'Lineup Edge' by removing unnecessary lambda application, improving code readability and performance.

Refactor reassess_edge function signature in global_func/reassess_edge.py to simplify parameters and update app.py to utilize the new signature, enhancing edge reassessment logic for player data processing.

function correction

Replace exposure_spread function call with reassess_edge in app.py to improve edge reassessment logic for player exposure handling during lineup processing.

Update position filtering in create_position_export_dict to include 'C/1B' and modify sport headers for WNBA and MLB across load functions, enhancing player data handling for CSV exports.

Enhance player data handling by updating load functions in load_dk_fd_file, load_file, and load_ss_file to incorporate sport-specific headers, improving CSV and lineup processing for various sports in app.py and global functions.

Refactor logic to set 'Position' and 'Team' as 'FLEX' for specific sports in app.py, ensuring proper handling of player data when a CSV file is loaded.

Implement logic to set 'Position' and 'Team' as 'FLEX' for specific sports in app.py, enhancing player data handling for GOLF, TENNIS, MMA, NASCAR, and F1.

Update position filtering logic in create_position_export_dict function in app.py to handle specific 'SP' position filtering, improving accuracy in player data exports.

Refine position filtering logic in create_position_export_dict function in app.py to remove trailing digits from column names, and update debug print statements to log structured output of filtered DataFrames for better data validation.

Add debug print statements in create_position_export_dict function in app.py to log position filters and filtered DataFrames, aiding in troubleshooting and data validation during export dictionary generation.

Add create_position_export_dict function in app.py to generate position-specific export dictionaries from CSV files, enhancing player data handling and mapping in export functionality.

Enhance exposure_spread function to include stacking_sports parameter, improving player selection logic for size 5 lineups in app.py and exposure_spread.py.

Replace reassess_edge function calls with predict_dupes in app.py for both working_frame and export_base, improving clarity and consistency in player lineup predictions.

Refactor reassess_lineup_edge function in reassess_edge.py to simplify the application of lineup edge calculations, enhancing code clarity and maintainability.

Rename reassess_edge function to reassess_lineup_edge in reassess_edge.py for clarity, and update references accordingly to improve code readability and maintainability.

Fix reassess_dupes function in reassess_edge.py to remove clipping on calculated values, ensuring accurate salary difference assessments for player lineups.

Update app.py to utilize prior_frame for reassessing edges in both working_frame and export_base, enhancing accuracy in player lineup metrics.

Refactor change_mask logic in reassess_edge.py to filter based solely on median differences, improving accuracy in edge assessments for player lineups.

Add reassess_edge function in reassess_edge.py for recalculating lineup metrics based on player performance, and update app.py to utilize this function for improved edge assessments in player lineups.

Add team comparison for filtering comparable players in exposure_spread.py, ensuring accurate player selection for size 5 lineups.

Add filtering for comparable players in exposure_spread.py based on team stacking for size 5 lineups, enhancing player selection accuracy.

Refactor position eligibility checks in exposure_spread.py to use explicit conditionals for each position, improving clarity and maintainability of the code.

Refactor position eligibility checks in exposure_spread.py to simplify logic for player positions, enhancing readability and maintainability. Update reassess_dupes function in reassess_edge.py to streamline salary difference calculations for improved performance.

Refactor predict_dupes function call in app.py to use parsed_frame and salary_max, and add reassess_edge function in reassess_edge.py for recalculating metrics based on differences in salary, median, and ownership.

Update exposure_spread function call in app.py to use export_base instead of working_frame, ensuring correct data handling for exposure calculations.

Refactor similarity score calculations in stratification_function to use quantiles instead of min/max thresholds, improving accuracy in target similarity score generation.

Update stratification sample range slider in app.py to allow a range input from 0.0 to 100.0, enhancing user control over sample selection.

Add stratification sample range slider to app.py and update stratification_function to accept threshold parameters for improved player selection control.

Remove docstrings from position eligibility functions in exposure_spread.py to streamline code and improve readability.

Add app.py to implement a Streamlit application for player analysis and portfolio management, featuring data loading, name matching, and exposure management functionalities.

Add application.py to implement Streamlit app for player analysis and portfolio management, including data loading, name matching, and exposure management features.

Remove debug print statements from app.py and exposure_spread.py to clean up code and improve performance during exposure evaluations.

Update exposure_player selection in app.py to use unique player names from projections_df, ensuring accurate data representation in exposure management.

Reinstate settings_base assignment in app.py after exposure_player debug print statement to ensure session state integrity during exposure evaluations.

Reinstate settings_base assignment in app.py after exposure_player debug print statement to maintain session state integrity during exposure evaluations.

Add debug print statement for exposure_player in app.py to assist with tracking state during exposure evaluations.